Gapped metals as thermoelectric materials revealed by high-throughput screening

Ricci, Francesco; Dunn, Alexander; Jain, Anubhav; Rignanese, Gian‐Marco; Hautier, Geoffroy

doi:10.1039/d0ta05197g

Cited by 25 publications

(16 citation statements)

References 108 publications

(121 reference statements)

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“…We consider the presence of energy gaps (or near-energy gaps) in occupied states close to the Fermi energy, which corresponds to an intrinsic eDOS that is similar to that of a degenerate semiconductor. Correspondingly, the metals exhibiting an energy gap in occupied states may exhibit transport-related properties such as electronic conductivity and a Seebeck coefficient that mimic a doped semiconductor, 34 broadening the materials search space. Indeed, this concept of metallic system having a gap close to the Fermi energy was introduced and studied in the research of potential transparent conductive materials, 35,[37][38][39] for applications in low-loss plasmonics, 40 and in catalysis.…”

Section: Modelmentioning

confidence: 99%

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Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings

Kong,

Ricci,

Guevarra

et al. 2021

Preprint

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Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

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Section: Modelmentioning

confidence: 99%

“…We demonstrate such acceleration of materials discovery with a use case based on identification of band gaps below but near the Fermi energy in metallic systems, which have been shown to be pertinent to thermoelectrics and transparent conductors. 34,35…”

Section: Introductionmentioning

confidence: 99%

Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings

Kong,

Ricci,

Guevarra

et al. 2021

Preprint

Self Cite

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“…Computations have greatly facilitated new TE materials discovery, 16–19 and their optimization through electronic doping. 13,20 At the same time, first-principles modeling of TE alloys has provided useful mechanistic insights; 10,21 however, a systematic computational framework for designing and optimizing TE alloys has not been demonstrated yet.…”

Section: Introductionmentioning

confidence: 99%

Computational design of thermoelectric alloys through optimization of transport and dopability

Balvanz

Baranets

et al. 2022

Mater. Horiz.

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“…[ 12–17 ] Usually, researchers need thousands of combinations in order to find the specific materials with appropriate compositions. [ 18–21 ] It is undoubtedly time‐consuming, expensive, and inefficient, so the traditional trial and error method can no longer meet the current experimental requirements. In the study of the PbTe‐PbSe‐SnTe‐SnSe system, it only takes 198 min to synthesize 121 samples using the high‐throughput (HT) experimental method, which is about 5–10 times faster than the traditional trial‐and‐error method.…”

Section: Introductionmentioning

confidence: 99%

Preparation and In‐Situ High‐Throughput Screening of Bi Thixotropic Ink

Yao

Qiu

et al. 2021

Adv Materials Inter

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Preparing thin‐layer thermoelectric (TE) material libraries through inkjet printing (IJP) combined with selective laser melting (SLM) is a feasible solution to achieve high‐throughput screening (HTS) of high‐performance TE materials. The preparation of stable printable elementary inks with solid particle size in micron‐level is a prerequisite for the realization of the above technical objective. In this work, a series of thixotropic inks containing micron‐sized Bi powder, mixed organic solvent of ethanol/ethylene glycol (EG), and polyurea rheological additive of BYK‐420 are prepared. According to the proposed screening principles for printability and sedimentation stability, the best composition of the Bi thixotropic ink is determined with a solid content of 2–10 vol%, a relative EG content of 2–8 vol%, and a relative additive BYK‐420 content of 2.5 vol%. The effectiveness of the above mentioned HTS method is further proven through the printability Z value and the sedimentation rate of the selected inks. This work provides an effective way for in‐situ high‐throughput screening the printability and sedimentation stability of thixotropic inks, even though the solid density is high and the particle size is in micron‐level. It would lay a solid foundation for further high‐throughput screening of high‐performance TE materials.

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Gapped metals as thermoelectric materials revealed by high-throughput screening

Abstract: The typical strategy to design high performance thermoelectric materials is to dope a semiconducting material until optimal properties are obtained. However, some known thermoelectric materials such as \ce{La3Te4}, \ce{Mo3Sb7}, \ce{Yb14MnSb11},...

Cited by 25 publications

References 108 publications

Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings

Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings

Computational design of thermoelectric alloys through optimization of transport and dopability

Preparation and In‐Situ High‐Throughput Screening of Bi Thixotropic Ink

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